Workshop at the UZH Reproducibility Day 2023
Center for Reproducible Science (CRS)
What is dynamic reporting?
How to do dynamic reporting?
Hands-on exercises
R MarkdownknitrQuartoJupyter Notebooks)R programming language
(> 60 other also possible)
.Rmd files (edit in RStudio, VS Code, etc.)
Markup language: Markdown
HTML, PDF, DOCX output formats (and more)
rmarkdown is an R package
Programming language: R
(> 60 other also possible)
.Rnw files (edit in RStudio, VS Code, etc.)
Markup languages: LaTeX
(+ HTML, Markdown, and more)
HTML, PDF, DOCX output formats (and more)
knitr is an R package
Programming language: R, Python, Julia
.qmd files (edit in RStudio, VS Code, etc.)
Markup language: Markdown
HTML, PDF, DOCX output formats (and more)
Evolution of R Markdown (introduced in 2022)
quarto is a separate program
rmarkdown → R users (beginner to advanced)knitr → R+LaTeX users (intermediate to advanced) quarto → R/Python/Julia users (beginner to advanced)
Dynamic Reporting & Reproducibility in Research
→ transdisciplinary course at UZH
R Markdown tutorial
→ good tutorial series from R Studio
R Markdown: The Definitive Guide
→ freely available book
knitr website
→ very comprehensive website from the knitr author
Quarto tutorial
→ good tutorial series from R Studio
Download the data sets on accidents in Zurich from https://github.crsuzh/dynamicReporting/XXXXX.
Explore the data from the 2020. Compute the number of accidents for each weekday and month. Make a chart of the number of accidents vs. weekday, and a chart of the number of accidents vs. month. Create an HTML report using either R Markdown or Quarto.
Now use the data from 2020 and 2021 and rerun your analysis. Create an updated report.
Bonus: Convert your report to a presentation.